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Binarized on the fly during the forward pass and never used for the inference process. In this case there are two areas to optimize for: training efficiency and inference efficiency. If I understand correctly, it stores the weights, gradients and second-moment estimates in FP32 like every other mixed-precision training (the Gopher paper has details on why storing them in FP32 is important), and quantized weights... - Source: Hacker News / 1 day ago
Related: https://medium.com/@maciej.pocwierz/how-an-empty-s3-bucket-can-make-your-aws-bill-explode-934a383cb8b1. - Source: Hacker News / 1 day ago
I was thinking of that a few weeks ago and wrote a (dumb) little story about it; I imagined Astro (SF mascot) is a ruthless mob boss controlling it all behind the scenes lol https://medium.com/@dannyisaphantom/benioff-astro-and-the-art-of-the-deal-692149f5dd36. - Source: Hacker News / 1 day ago
Thanks for sharing, I like the approach and it makes a lot of sense for the problem space. Especially using existing products vs building/hosting your own. I was however tripped up by this sentence close to the beginning: > we encountered a significant challenge with RAG: relying solely on vector search (even using both dense and sparse vectors) doesn’t always deliver satisfactory results for certain queries. Not... - Source: Hacker News / 1 day ago
This is a better one: https://medium.com/@lordmoma/so-you-think-you-know-git-673f9c4b0792. - Source: Hacker News / 5 days ago
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